



'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase_  :							List[Any]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							Union[str, Any]             =    {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Dict						  =					'gpt_neo'
						_lowerCAmelCase  : Union[str, Any]						  =					['past_key_values']
						_lowerCAmelCase  : List[Any]						  =					{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(	self :   List[str]					,     lowerCAmelCase__ :   List[str]=5_02_57					,     lowerCAmelCase__ :   Any=20_48					,     lowerCAmelCase__ :   Union[str, Any]=20_48					,     lowerCAmelCase__ :   List[str]=24					,     lowerCAmelCase__ :   List[Any]=[[["global", "local"], 12]]					,     lowerCAmelCase__ :   str=16					,     lowerCAmelCase__ :   Any=None					,     lowerCAmelCase__ :   Optional[int]=2_56					,     lowerCAmelCase__ :   Optional[Any]="gelu_new"					,     lowerCAmelCase__ :   Tuple=0.0					,     lowerCAmelCase__ :   List[str]=0.0					,     lowerCAmelCase__ :   Dict=0.0					,     lowerCAmelCase__ :   Dict=0.1					,     lowerCAmelCase__ :   Union[str, Any]=1e-5					,     lowerCAmelCase__ :   Dict=0.02					,     lowerCAmelCase__ :   Dict=True					,     lowerCAmelCase__ :   int=5_02_56					,     lowerCAmelCase__ :   Tuple=5_02_56					,     **lowerCAmelCase__ :   Tuple					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      vocab_size
													SCREAMING_SNAKE_CASE       :   Tuple         =      max_position_embeddings
													SCREAMING_SNAKE_CASE       :   List[str]         =      hidden_size
													SCREAMING_SNAKE_CASE       :   List[Any]         =      num_layers
													SCREAMING_SNAKE_CASE       :   Tuple         =      num_heads
													SCREAMING_SNAKE_CASE       :   str         =      intermediate_size
													SCREAMING_SNAKE_CASE       :   Dict         =      window_size
													SCREAMING_SNAKE_CASE       :   str         =      activation_function
													SCREAMING_SNAKE_CASE       :   Any         =      resid_dropout
													SCREAMING_SNAKE_CASE       :   Dict         =      embed_dropout
													SCREAMING_SNAKE_CASE       :   Tuple         =      attention_dropout
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      classifier_dropout
													SCREAMING_SNAKE_CASE       :   int         =      layer_norm_epsilon
													SCREAMING_SNAKE_CASE       :   str         =      initializer_range
													SCREAMING_SNAKE_CASE       :   List[str]         =      use_cache

													SCREAMING_SNAKE_CASE       :   List[str]         =      bos_token_id
													SCREAMING_SNAKE_CASE       :   Dict         =      eos_token_id

													SCREAMING_SNAKE_CASE       :   List[str]         =      attention_types
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.expand_attention_types_params(lowerCAmelCase__					)

													if len(self.attention_layers					) != self.num_layers:
																				raise ValueError(
																				    '''Configuration for convolutional module is incorrect. '''
																				    '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
																				    F"""but is `len(config.attention_layers) = {len(self.attention_layers					)}`, """
																				    F"""`config.num_layers = {self.num_layers}`. """
																				    '''`config.attention_layers` is prepared using `config.attention_types`. '''
																				    '''Please verify the value of `config.attention_types` argument.'''					)

													super().__init__(bos_token_id=lowerCAmelCase__					,     eos_token_id=lowerCAmelCase__					,     **lowerCAmelCase__					)







						@staticmethod
						def        __lowercase     (	lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      []
													for item in attention_types:
																				for _ in range(item[1]					):
																											attentions.extend(item[0]					)
													return attentions


def        UpperCAmelCase (    A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Dict       ,		A	:					int					):
							import torch

							SCREAMING_SNAKE_CASE       :   Any         =      input.size()
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      len(A					)
							SCREAMING_SNAKE_CASE       :   str         =      shape[dimension]

							SCREAMING_SNAKE_CASE       :   Dict         =      torch.arange(0       ,		A       ,		A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      torch.div(sizedim - size       ,		A       ,		rounding_mode='''floor'''					) + 1
							SCREAMING_SNAKE_CASE       :   Dict         =      torch.arange(A					) + low_indices[:min_length][:, None]

							SCREAMING_SNAKE_CASE       :   str         =      [slice(A					)] * rank
							SCREAMING_SNAKE_CASE       :   Dict         =      indices
							SCREAMING_SNAKE_CASE       :   Tuple         =      input[s]

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      list(range(0       ,		rank + 1					)					)
							perm.append(perm.pop(dimension + 1					)					)

							return sliced.permute(A					)
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Optional[Any]					):
							import torch

							SCREAMING_SNAKE_CASE       :   int         =      torch.arange(1       ,		A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.remainder(A       ,		A					)
							SCREAMING_SNAKE_CASE       :   str         =      remainders == 0
							SCREAMING_SNAKE_CASE       :   int         =      candidates[divisor_indices]
							SCREAMING_SNAKE_CASE       :   int         =      torch.max(A					)
							return largest_divisor, torch.div(A       ,		A       ,		rounding_mode='''floor'''					)






class 						lowerCamelCase_				(       snake_case_      ):







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
													if self.use_past:
																				self.fill_with_past_key_values_(lowerCAmelCase__					,     direction='''inputs'''					)
																				SCREAMING_SNAKE_CASE       :   Any         =      {0: '''batch''', 1: '''past_sequence + sequence'''}
													else:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      {0: '''batch''', 1: '''sequence'''}

													return common_inputs







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													return self._config.num_heads







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   PreTrainedTokenizer					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[TensorType] = None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      super(lowerCAmelCase__					,     self					).generate_dummy_inputs(
													    lowerCAmelCase__					,     batch_size=lowerCAmelCase__					,     seq_length=lowerCAmelCase__					,     is_pair=lowerCAmelCase__					,     framework=lowerCAmelCase__					)

													# We need to order the input in the way they appears in the forward()
													SCREAMING_SNAKE_CASE       :   List[str]         =      OrderedDict({'''input_ids''': common_inputs['''input_ids''']}					)

													# Need to add the past_keys
													if self.use_past:
																				if not is_torch_available():
																											raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
																				else:
																											import torch

																											SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      common_inputs['''input_ids'''].shape
																											# Not using the same length for past_key_values
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      seqlen + 2
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      (
																											    batch,
																											    self.num_attention_heads,
																											    past_key_values_length,
																											    self._config.hidden_size // self.num_attention_heads,
																											)
																											SCREAMING_SNAKE_CASE       :   int         =      [
																											    (torch.zeros(lowerCAmelCase__					), torch.zeros(lowerCAmelCase__					)) for _ in range(self.num_layers					)
																											]

													SCREAMING_SNAKE_CASE       :   Any         =      common_inputs['''attention_mask''']
													if self.use_past:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      ordered_inputs['''attention_mask'''].dtype
																				SCREAMING_SNAKE_CASE       :   List[str]         =      torch.cat(
																				    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__					,     lowerCAmelCase__					,     dtype=lowerCAmelCase__					)]					,     dim=1					)

													return ordered_inputs







						@property
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													return 13









'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							List[Any]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      torch.load(A       ,		map_location='''cpu'''					)
							if "model" in sd.keys():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.load(A       ,		map_location='''cpu'''					)['''model''']

							# pop unnecessary weights
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      [
							    '''decoder.version''',
							    '''decoder.output_projection.weight''',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   int         =      {
							    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
							    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
							    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
							}
							for old_key, new_key in keys_to_rename.items():
														if old_key in sd:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      list(sd.keys()					)
							for key in keys:
														if ".qkv_proj." in key:
																					SCREAMING_SNAKE_CASE       :   str         =      sd[key]
																					# We split QKV in separate Q,K,V

																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key.replace('''.qkv_proj.'''       ,		'''.q_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   str         =      key.replace('''.qkv_proj.'''       ,		'''.k_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   Any         =      key.replace('''.qkv_proj.'''       ,		'''.v_proj.'''					)

																					SCREAMING_SNAKE_CASE       :   Tuple         =      value.shape[0]
																					assert depth % 3 == 0
																					# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																					# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      torch.split(A       ,		depth // 3       ,		dim=0					)

																					SCREAMING_SNAKE_CASE       :   List[str]         =      q
																					SCREAMING_SNAKE_CASE       :   List[str]         =      k
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      v
																					del sd[key]

							return sd
@torch.no_grad()
def        UpperCAmelCase (    A	:					int       ,		A	:					Union[str, Any]       ,		A	:					Dict=None					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_checkpoint(A					)

							if config is not None:
														SCREAMING_SNAKE_CASE       :   Tuple         =      OPTConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   int         =      OPTConfig()

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OPTModel(A					).half().eval()
							model.load_state_dict(A					)

							# Check results
							Path(A					).mkdir(exist_ok=A					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[str]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--fairseq_path',
							    type=str,
							    help=(
							        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
							        ' https://huggingface.co/models?other=opt_metasq'
							    ),
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
							lowerCAmelCase_  :							str             =    parser.parse_args()
							convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




